CN117031331A - Battery impedance estimation method and device and computer equipment - Google Patents
Battery impedance estimation method and device and computer equipment Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 49
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- 238000007599 discharging Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 7
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- 238000012216 screening Methods 0.000 claims description 5
- 238000001514 detection method Methods 0.000 claims description 3
- 208000028659 discharge Diseases 0.000 description 142
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 11
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/3644—Constructional arrangements
- G01R31/3648—Constructional arrangements comprising digital calculation means, e.g. for performing an algorithm
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/385—Arrangements for measuring battery or accumulator variables
- G01R31/387—Determining ampere-hour charge capacity or SoC
- G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements
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Abstract
The application relates to a battery impedance estimation method, a device and computer equipment, wherein the method comprises the following steps: acquiring actual discharge information of a target battery; when the actual discharge information does not meet the high-rate discharge condition, determining the actual impedance information of the target battery according to the first impedance estimation model and the actual discharge information; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to the second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by a sample battery corresponding to the target battery under the high-rate discharge condition. Based on the scheme, when the actual discharge information meets the high-rate discharge condition, the terminal selects a model obtained by training based on training data information of the sample battery under the high-rate discharge condition, carries out impedance estimation on the target battery, and effectively improves the accuracy of nonlinear characteristic estimation on the target battery.
Description
Technical Field
The present application relates to the field of battery management technologies, and in particular, to a method and an apparatus for estimating battery impedance, and a computer device.
Background
Along with the gradual pollution of fossil energy to the environment and the requirement of energy transformation, novel environment-friendly energy systems such as lithium ion batteries and the like are widely applied. However, the lithium ion battery may cause phenomena such as aging acceleration of the battery due to unreasonable discharge in the use process, so that the battery is scrapped too quickly. Therefore, the estimation of the available power of the battery by the battery management system is of great importance for safe operation of the battery. Wherein the internal resistance of the battery is an important parameter for the estimation of the available power of the battery.
Currently, there is still little research on estimation of the internal resistance of a battery. For the available power estimation, a linear equivalent circuit model with lower calculation force requirements is generally adopted, and the battery characteristics obtained by simulation based on the linear equivalent circuit model with lower calculation force requirements cannot well accord with the nonlinear characteristics of the battery under the condition of high-rate current, so that when impedance estimation is carried out, the problem that the error between an estimation result and an actual result is larger exists.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a battery impedance estimation method, apparatus, and computer device that can accurately perform battery impedance estimation.
In a first aspect, the present application provides a method of estimating battery impedance. The method comprises the following steps:
acquiring actual discharge information of a target battery; the actual discharge information comprises real-time battery current and discharge time;
detecting whether the actual discharge information meets a high-rate discharge condition;
when the actual discharge information does not meet the high-rate discharge condition, determining the actual impedance information of the target battery according to a first impedance estimation model and the actual discharge information; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to a second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by the sample battery corresponding to the target battery under the high-rate discharge condition.
In one embodiment, the method further comprises:
when the real-time discharge current is larger than the critical current and the discharge time is larger than the critical time, determining that the actual discharge information meets the high-rate discharge condition;
and when the real-time discharge current is smaller than or equal to the critical current or the discharge time is smaller than or equal to the critical time, determining that the actual discharge information does not meet the high-rate discharge condition.
In one embodiment, the method further comprises:
carrying out preset discharge by using the sample battery to obtain battery impedance test information; the battery impedance test information comprises test discharge time and test current; the sample battery and the target battery are of the same type;
determining target test information in the battery impedance test information according to preset screening conditions; the target test information comprises critical current and critical time;
taking the battery impedance test information corresponding to the test current being larger than the critical current and the test discharge time being larger than the critical time as the training data information;
and training the initial impedance estimation model according to the training data information to obtain the second impedance estimation model.
In one embodiment, the training the initial impedance estimation model according to the training data information to obtain the second impedance estimation model includes:
training the initial impedance estimation model through a genetic algorithm according to the training data information to obtain trained model parameters;
and obtaining the second impedance pre-estimated model based on the trained model parameters.
In one embodiment, the second impedance estimation model is:
Z＝z(t)+C(1-e (AI+B)(t-T) )
wherein z (t) is the model parameter after training of the first impedance estimation model A, B, C.
In one embodiment, the performing the preset discharge by using the sample battery to obtain the battery impedance test information includes:
and continuously discharging the sample battery under different current multiplying factors to obtain battery impedance test information of the impedance of the sample battery changing along with time under the different current multiplying factors.
In a second aspect, the application further provides a battery impedance estimation device. The device comprises:
the acquisition module is used for acquiring the actual discharge information of the target battery; the actual discharge information comprises real-time battery current and discharge time;
the detection module is used for detecting whether the actual discharge information meets a high-rate discharge condition or not;
the estimation module is used for determining the actual impedance information of the target battery according to a first impedance estimation model and the actual discharge information when the actual discharge information does not meet the high-rate discharge condition; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to a second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by the sample battery corresponding to the target battery under the high-rate discharge condition.
In one embodiment, the battery impedance estimation device further includes:
the first determining module is used for determining that the actual discharge information meets the high-rate discharge condition when the real-time discharge current is larger than the critical current and the discharge time is larger than the critical time;
and the second determining module is used for determining that the actual discharge information does not meet the high-rate discharge condition when the real-time discharge current is smaller than or equal to the critical current or the discharge time is smaller than or equal to the critical time.
In one embodiment, the battery impedance estimation device further includes:
the sample data acquisition module is used for carrying out preset discharge by utilizing the sample battery to obtain battery impedance test information; the battery impedance test information comprises test discharge time and test current; the sample battery and the target battery are of the same type;
the target information determining module is used for determining target test information in the battery impedance test information according to preset screening conditions; the target test information comprises critical current and critical time;
the training data determining module is used for taking the battery impedance test information corresponding to the test current being larger than the critical current and the test discharging time being larger than the critical time as the training data information;
and the model training module is used for training the initial impedance estimation model according to the training data information to obtain the second impedance estimation model.
In one embodiment, the model training module is specifically configured to:
training the initial impedance estimation model through a genetic algorithm according to the training data information to obtain trained model parameters;
and obtaining the second impedance pre-estimated model based on the trained model parameters.
In one embodiment, the second impedance estimation model is:
Z＝z(t)+C(1-e (AI+B)(t-T) )
wherein z (t) is the model parameter after training of the first impedance estimation model A, B, C.
In one embodiment, the sample data acquisition module is specifically configured to:
and continuously discharging the sample battery under different current multiplying factors to obtain battery impedance test information of the impedance of the sample battery changing along with time under the different current multiplying factors.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the first aspect described above when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the first aspect described above.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprising a computer program which, when executed by a processor, carries out the steps of the first aspect described above.
The battery impedance estimation method, the device and the computer equipment acquire the actual discharge information of the target battery; when the actual discharge information does not meet the high-rate discharge condition, determining the actual impedance information of the target battery according to the first impedance estimation model and the actual discharge information; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to the second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by a sample battery corresponding to the target battery under the high-rate discharge condition. Based on the method, the terminal judges whether the actual discharge information meets the high-rate discharge condition or not, selects an impedance estimation model matched with the judgment result to carry out impedance estimation on the target battery, and selects a model obtained by training based on training data information of the sample battery under the high-rate discharge condition when the actual discharge information meets the high-rate discharge condition to carry out impedance estimation on the target battery, so that the accuracy of nonlinear characteristic estimation on the target battery is effectively improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments or the conventional techniques of the present application, the drawings required for the descriptions of the embodiments or the conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.
FIG. 1 is a flow chart of a method of estimating battery impedance in one embodiment;
FIG. 2 is a flowchart of a method for estimating battery impedance according to another embodiment;
FIG. 3 is a first-order equivalent circuit model of a lithium ion battery;
fig. 4a is a schematic diagram of a continuous discharge voltage characteristic of a lithium ion battery under a low-rate current condition;
fig. 4b is a schematic diagram of voltage characteristics of a lithium ion battery in continuous discharge under a high-rate current condition;
FIG. 5a is a diagram showing the impedance characteristics of the battery when the battery is continuously discharged for 5s under different current rates;
FIG. 5b is a schematic diagram showing the impedance characteristics of the battery when the battery is discharged under constant current for 10s and 15s under different multiplying power conditions;
FIG. 6 is a block diagram of a battery impedance estimating apparatus in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment of the present application, as shown in fig. 1, a battery impedance estimation method is provided, and this embodiment is exemplified by the method applied to a terminal (may be referred to as a management terminal), it will be understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and implemented through interaction between the terminal and the server. The terminal may be, but not limited to, various personal computers and notebook computers. The server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers. In this embodiment, the method includes the steps of:
step 101, obtaining actual discharge information of a target battery; the actual discharge information includes real-time battery current and discharge time.
The specific type of the target battery is not limited, and the target battery can be selected according to requirements. Further, the terminal can obtain the actual discharge information of the target battery through any form of discharge experiment.
Step 102, detecting whether the actual discharge information satisfies the high-rate discharge condition.
The terminal needs to judge whether the target battery is in a high-rate discharge stage or not according to the actual discharge information of the target battery, namely whether the target battery is in an increasing trend of impedance along with the current increase.
Step 103, when the actual discharge information does not meet the high-rate discharge condition, determining the actual impedance information of the target battery according to the first impedance estimation model and the actual discharge information; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to the second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by the sample battery corresponding to the target battery under the high-rate discharge condition.
When the terminal determines that the actual discharge information does not meet the high-rate discharge condition, it indicates that the impedance of the target battery at the moment is in a decreasing trend along with the increase of the current, and the target battery has a linear characteristic at the moment, and the lithium ion battery can be transmitted to a first-order equivalent circuit model (as shown in fig. 3, wherein E is an open circuit voltage, R is an ohmic internal resistance, R 1 Is polarized to have internal resistance, C 1 Internal polarization resistance) is used as a first impedance estimation model to perform battery impedance estimation. When the terminal determines that the actual discharge information meets the high-rate discharge condition, the terminal indicates that the impedance of the target battery is in an increasing trend along with the current increase, and the target battery has nonlinear characteristics. In order to accurately estimate the nonlinear battery characteristics, the terminal needs to be controlled according to the target electricityTraining data information of the sample batteries of the same type in the battery under the high-rate discharge condition is used for model training to obtain a second impedance estimation model corresponding to the target battery, and further accurate impedance estimation is carried out on the target battery according to actual discharge information meeting the high-rate discharge condition.
In the battery impedance estimation method, the terminal accurately estimates the impedance of the target battery by judging whether the actual discharge information meets the high-rate discharge condition or not and selecting an impedance estimation model matched with a judgment result. Specifically, when the actual discharge information does not meet the high-rate discharge condition, the terminal selects a first-order equivalent circuit model as a first impedance estimation model to estimate the linear characteristics of the target battery; when the discharge information meets the high-rate discharge condition, the terminal selects a model obtained by training based on training data information of the sample battery under the high-rate discharge condition as a second impedance estimation model, and estimates nonlinear characteristics of the target battery. Based on the above, the method provided by the embodiment of the application can effectively estimate the nonlinear characteristics of the battery under the high-rate discharge condition, and further can obtain an accurate battery impedance estimated value.
In one embodiment of the present application, the battery impedance estimation method further includes:
when the real-time discharge current is larger than the critical current and the discharge time is larger than the critical time, determining that the actual discharge information meets the high-rate discharge condition;
and when the real-time discharge current is smaller than or equal to the critical current or the discharge time is smaller than or equal to the critical time, determining that the actual discharge information does not meet the high-rate discharge condition.
Wherein the critical current and critical time are determined in the second impedance estimation model.
Specifically, when the real-time discharge current is greater than the critical current and the discharge time is greater than the critical time, it is indicated that the impedance of the target battery is in a stage of increasing as the current multiplying power increases. The solid-phase diffusion theory of the battery indicates that the lithium ion diffusion rate is larger than the maximum diffusion rate of the lithium ion in the solid-phase particles under the high-rate current of the battery, so that a higher concentration gradient of the lithium ion can be generated in the solid-phase diffusion, and when the impedance tends to increase along with the current, the voltage of the battery can be exponentially reduced along with the time, namely the battery has nonlinear characteristics.
According to the battery impedance estimation method, whether the target battery is in a high-rate discharge state can be accurately judged according to the comparison result of the actual battery electric quantity and the discharge time in the actual discharge information of the target battery, the critical current and the critical time, and further, when the target battery meets the high-rate discharge condition, the nonlinear characteristics can be accurately estimated by adopting the second impedance estimation model.
In one embodiment of the present application, as shown in fig. 2, the battery impedance estimation method further includes:
step 201, performing preset discharge by using a sample battery to obtain battery impedance test information; the battery impedance test information comprises test discharge time and test current; the sample cell and the target cell are of the same type.
Specifically, the terminal needs to select a plurality of batteries which are the same type as the target battery and have good consistency as sample batteries, and then carries out high-rate continuous discharge on the sample batteries to obtain battery impedance test information. It should be noted that, the battery impedance test information may be data information of battery impedance changing with time under different current multiplying power.
Step 202, determining target test information in battery impedance test information according to preset screening conditions; the target test information includes a critical current and a critical time.
Specifically, the terminal may determine, as the target test information, a critical current and a critical time corresponding to a trend that the battery impedance exhibits an increase with an increase in current in the battery impedance test information by including, but not limited to, a control variable method. Based on this, the terminal may determine that the battery impedance is in a state of increasing with an increase in current when the actual battery current of the target battery is greater than the critical current and the discharge time is greater than the critical time, i.e., when the characteristics of the target battery are in a nonlinear variation state.
And 203, using the battery impedance test information corresponding to the test current being greater than the critical current and the test discharge time being greater than the critical time as training data information.
Specifically, the battery impedance test information corresponding to the test current extracted by the terminal being greater than the critical current and the test discharge time being greater than the critical time is the battery impedance test information corresponding to the sample battery when the characteristics of the sample battery are in nonlinear variation.
And 204, training the initial impedance estimation model according to the training data information to obtain a second impedance estimation model.
Specifically, the terminal trains the initial impedance estimation model according to the battery impedance test information when the battery characteristics are in nonlinear variation, and a second impedance estimation model capable of accurately estimating the nonlinear variation impedance can be obtained.
In the above battery impedance estimation method, the terminal trains the initial impedance estimation model based on the battery impedance test information corresponding to the sample battery of the same type as the target battery, when the test current is greater than the critical current and the test discharge time is greater than the critical time, to obtain the second impedance estimation model. By the method, the accuracy of the second impedance estimation model in estimating the impedance of the target battery meeting the high-rate discharge condition can be improved.
In one embodiment of the present application, the step 204 trains the initial impedance estimation model according to training data information to obtain a second impedance estimation model, including:
training the initial impedance estimated model through a genetic algorithm according to the training data information to obtain trained model parameters;
and obtaining a second impedance pre-estimated model based on the trained model parameters.
The terminal stores an initial impedance estimation model constructed based on a battery solid-phase diffusion theory. When the current multiplying power is too high, the battery voltage can be rapidly reduced along with time, so that the battery impedance also presents an exponential increase, namely the initial impedance estimation model is a model which increases exponentially. Specifically, the terminal trains the initial impedance estimation model according to the genetic algorithm and training data information, so that each parameter information of the initial impedance estimation model can be accurately determined, and a second impedance estimation model is further obtained.
In the battery impedance estimation method, the parameters of the initial impedance estimation model are forcedly optimized through the genetic algorithm, so that the accuracy of the second impedance estimation model can be improved.
In one embodiment of the present application, the second impedance estimation model is:
Z＝z(t)+C(1-e (AI+B)(t-T) )
where z (t) is the model parameter after training of the first impedance predictive model A, B, C.
Specifically, the first impedance estimation model is:
wherein A is the active area of the electrode; a, a c And a a Is the charge transfer coefficient (a) of the cathode and anode c +a a =1); n is the number of electrons involved in the electrode reaction; f is Faraday assuming that the charge transfer coefficients of the anode and cathode are equal (a c ＝a a ) (reasonable for lithium ion batteries), and with I 0 ＝2·A·i 0 Andtwo alternatives.
In an embodiment of the present application, the step 201 of performing the preset discharge by using the sample battery to obtain the battery impedance test information includes:
and continuously discharging the sample battery under different current multiplying factors to obtain battery impedance test information of the impedance of the sample battery changing along with time under different current multiplying factors.
The terminal can acquire the condition that the voltage corresponding to each current multiplying power changes along with time when the sample battery continuously discharges under different current multiplying powers, and then can determine battery impedance test information of impedance changing along with time under each current multiplying power.
According to the battery impedance estimation method, the accuracy of determining the critical current and the critical time of the terminal can be improved by setting different current multiplying powers and performing constant current discharge on the sample battery under the different current multiplying powers.
In one embodiment of the application, the target battery is a 42Ah ternary lithium ion battery of a manufacturer. The cell size was 14.83 cm. Times.2.8 cm. Times.9.28 cm. The standard voltage of the battery monomer is 3.7V, the cycle life is more than 1500 times, the internal resistance of the battery core is 0.75mΩ, and the working temperature range is-20 ℃ to 60 ℃. And the terminal continuously and constantly discharges the sample battery with good consistency with the target battery under different current multiplying factors to obtain battery impedance test information of the sample battery, wherein the impedance of the sample battery changes with time under different current multiplying factors. And the terminal determines a current critical multiplying power point C and a corresponding time point T of the current critical multiplying power point C, wherein the battery impedance increases along with the current increase, in the battery impedance test information.
As shown in fig. 5a, which shows the change of the impedance characteristics of the battery when the battery is continuously discharged for 5s under different current multiplying power conditions, and fig. 5b, which shows the change of the impedance characteristics of the battery when the battery is respectively discharged for 10s and 15s under different current multiplying power conditions, the impedance of the 42Ah ternary battery is 10s later under the continuous discharging condition, and when the current multiplying power is greater than 6C, the impedance of the battery tends to increase with the increase of the current, so that t=10s and c=6c can be determined.
When the current multiplying power is lower than C or the discharging time is lower than T, the impedance of the battery still shows a descending trend along with the current increase, and no trend change occurs. As shown in fig. 4 a. Thus, the battery impedance can still be represented by the conventional impedance formula:
when the current multiplying power is higher than C and the discharging time is higher than T, the increasing trend of the battery impedance changes. In the battery impedance model, the difference between the battery state of charge on the surface of the battery material and the average battery state of charge increases according to an exponential rule, and the exponential speed is linearly related to the multiplying power.
As shown in fig. 4b, when the current multiplying power is too high, the battery voltage will decrease rapidly with time, so that the battery impedance also increases exponentially, that is:
Z＝z(t)+C(1-e (AI+B)(t-T) )
where z (t) is the model parameter after training of the first impedance predictive model A, B, C.
Based on this, the terminal has established a battery impedance model, and can perform real-time impedance estimation according to the obtained real-time battery current and discharge time of the target battery.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a battery impedance estimation device for realizing the above related battery impedance estimation method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the battery impedance estimation device or devices provided below may be referred to the limitation of the battery impedance estimation method hereinabove, and will not be repeated here.
As shown in fig. 6, the present application also provides a battery impedance estimating apparatus 600, comprising:
an obtaining module 610, configured to obtain actual discharge information of the target battery; the actual discharge information comprises real-time battery current and discharge time;
a detection module 620, configured to detect whether the actual discharge information meets a high-rate discharge condition;
the estimation module 630 is configured to determine, when the actual discharge information does not meet the high-rate discharge condition, actual impedance information of the target battery according to the first impedance estimation model and the actual discharge information; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to the second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by the sample battery corresponding to the target battery under the high-rate discharge condition.
In one embodiment of the present application, the battery impedance estimating apparatus further includes:
the first determining module is used for determining that the actual discharge information meets the high-rate discharge condition when the real-time discharge current is larger than the critical current and the discharge time is larger than the critical time;
and the second determining module is used for determining that the actual discharge information does not meet the high-rate discharge condition when the real-time discharge current is smaller than or equal to the critical current or the discharge time is smaller than or equal to the critical time.
In one embodiment of the present application, the battery impedance estimating apparatus further includes:
the sample data acquisition module is used for carrying out preset discharge by utilizing the sample battery to obtain battery impedance test information; the battery impedance test information comprises test discharge time and test current; the sample battery and the target battery are of the same type;
the target information determining module is used for determining target test information in the battery impedance test information according to preset screening conditions; the target test information comprises critical current and critical time;
the training data determining module is used for taking battery impedance testing information corresponding to the fact that the testing current is larger than the critical current and the testing discharging time is larger than the critical time as training data information;
the model training module is used for training the initial impedance estimation model according to the training data information to obtain a second impedance estimation model.
In one embodiment of the application, the model training module is specifically configured to:
training the initial impedance estimated model through a genetic algorithm according to the training data information to obtain trained model parameters;
and obtaining a second impedance pre-estimated model based on the trained model parameters.
In one embodiment of the present application, the second impedance estimation model is:
Z＝z(t)+C(1-e (AI+B)(t-T) )
where z (t) is the model parameter after training of the first impedance predictive model A, B, C.
In one embodiment of the present application, the sample data acquisition module is specifically configured to:
and continuously discharging the sample battery under different current multiplying factors to obtain battery impedance test information of the impedance of the sample battery changing along with time under different current multiplying factors.
In one embodiment of the present application, a computer device, which may be a terminal, is provided, and an internal structure diagram thereof may be as shown in fig. 7. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a method of estimating battery impedance. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 7 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment of the application, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above method when executing the computer program.
In one embodiment of the application, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, implements the steps of the above method.
The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method of estimating battery impedance, the method comprising:
acquiring actual discharge information of a target battery; the actual discharge information comprises real-time battery current and discharge time;
detecting whether the actual discharge information meets a high-rate discharge condition;
when the actual discharge information does not meet the high-rate discharge condition, determining the actual impedance information of the target battery according to a first impedance estimation model and the actual discharge information; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to a second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by the sample battery corresponding to the target battery under the high-rate discharge condition.
2. The battery impedance estimation method according to claim 1, characterized in that the method further comprises:
when the real-time discharge current is larger than the critical current and the discharge time is larger than the critical time, determining that the actual discharge information meets the high-rate discharge condition;
and when the real-time discharge current is smaller than or equal to the critical current or the discharge time is smaller than or equal to the critical time, determining that the actual discharge information does not meet the high-rate discharge condition.
3. The battery impedance estimation method according to claim 1, characterized in that the method further comprises:
carrying out preset discharge by using the sample battery to obtain battery impedance test information; the battery impedance test information comprises test discharge time and test current; the sample battery and the target battery are of the same type;
determining target test information in the battery impedance test information according to preset screening conditions; the target test information comprises critical current and critical time;
taking the battery impedance test information corresponding to the test current being larger than the critical current and the test discharge time being larger than the critical time as the training data information;
and training the initial impedance estimation model according to the training data information to obtain the second impedance estimation model.
4. The method of estimating battery impedance according to claim 3, wherein training the initial impedance estimation model according to the training data information to obtain the second impedance estimation model comprises:
training the initial impedance estimation model through a genetic algorithm according to the training data information to obtain trained model parameters;
and obtaining the second impedance pre-estimated model based on the trained model parameters.
5. The method of estimating battery impedance according to claim 4, wherein the second impedance estimation model is:
Z＝z(t)+C(1-e (AI+B)(t-T) )
wherein z (t) is the model parameter after training of the first impedance estimation model A, B, C.
6. The method for estimating battery impedance according to claim 3, wherein said performing a preset discharge using the sample battery to obtain battery impedance test information comprises:
and continuously discharging the sample battery under different current multiplying factors to obtain battery impedance test information of the impedance of the sample battery changing along with time under the different current multiplying factors.
7. A battery impedance estimation device, the device comprising:
the acquisition module is used for acquiring the actual discharge information of the target battery; the actual discharge information comprises real-time battery current and discharge time;
the detection module is used for detecting whether the actual discharge information meets a high-rate discharge condition or not;
the estimation module is used for determining the actual impedance information of the target battery according to a first impedance estimation model and the actual discharge information when the actual discharge information does not meet the high-rate discharge condition; when the actual discharge information meets the high-rate discharge condition, determining the actual impedance information of the target battery according to a second impedance estimation model and the actual discharge information; the second impedance estimation model is obtained through training according to training data information obtained by the sample battery corresponding to the target battery under the high-rate discharge condition.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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